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Conference Paper: One-pass wavelet synopses for maximum-error metrics

TitleOne-pass wavelet synopses for maximum-error metrics
Authors
Issue Date2005
Citation
Vldb 2005 - Proceedings Of 31St International Conference On Very Large Data Bases, 2005, v. 1, p. 421-432 How to Cite?
AbstractWe study the problem of computing wavelet-based synopses for massive data sets in static and streaming environments. A compact representation of a data set is obtained after a thresholding process is applied on the coefficients of its wavelet decomposition. Existing polynomial-time thresholding schemes that minimize maximum error metrics are disadvantaged by impracticable time and space complexities and are not applicable in a data stream context. This is a cardinal issue, as the problem at hand in its most practically interesting form involves the time-efficient approximation of huge amounts of data, potentially in a streaming environment. In this paper we fill this gap by developing efficient and practicable wavelet thresholding algorithms for maximum-error metrics, for both a static and a streaming case. Our algorithms achieve near-optimal accuracy and superior runtime performance, as our experiments show, under frugal space requirements in both contexts.
Persistent Identifierhttp://hdl.handle.net/10722/93155
References

 

DC FieldValueLanguage
dc.contributor.authorKarras, Pen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.date.accessioned2010-09-25T14:52:33Z-
dc.date.available2010-09-25T14:52:33Z-
dc.date.issued2005en_HK
dc.identifier.citationVldb 2005 - Proceedings Of 31St International Conference On Very Large Data Bases, 2005, v. 1, p. 421-432en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93155-
dc.description.abstractWe study the problem of computing wavelet-based synopses for massive data sets in static and streaming environments. A compact representation of a data set is obtained after a thresholding process is applied on the coefficients of its wavelet decomposition. Existing polynomial-time thresholding schemes that minimize maximum error metrics are disadvantaged by impracticable time and space complexities and are not applicable in a data stream context. This is a cardinal issue, as the problem at hand in its most practically interesting form involves the time-efficient approximation of huge amounts of data, potentially in a streaming environment. In this paper we fill this gap by developing efficient and practicable wavelet thresholding algorithms for maximum-error metrics, for both a static and a streaming case. Our algorithms achieve near-optimal accuracy and superior runtime performance, as our experiments show, under frugal space requirements in both contexts.en_HK
dc.languageengen_HK
dc.relation.ispartofVLDB 2005 - Proceedings of 31st International Conference on Very Large Data Basesen_HK
dc.titleOne-pass wavelet synopses for maximum-error metricsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-33745616563en_HK
dc.identifier.hkuros103331en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33745616563&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1en_HK
dc.identifier.spage421en_HK
dc.identifier.epage432en_HK
dc.identifier.scopusauthoridKarras, P=14028488200en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK

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